![](/images/graphics-bg.png)
Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks
Author
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-19
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Subcontractor performance directly affects project success.
The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project.
This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance.
EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs).
FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry.
NNs are used to identify the association between previous performance and future status when predicting subcontractor performance.
GAs are optimizing parameters required in FL and NNs.
EFNNs encode FL and NNs using floating numbers to shorten the length of a string.
A multi-cut-point crossover operator is used to explore the parameter and retain solution legality.
Finally, the applicability of the proposed EFNNs is validated using real subcontractors.
The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases.
Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance.
The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.
American Psychological Association (APA)
Ko, Chien-Ho. 2013. Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1012803
Modern Language Association (MLA)
Ko, Chien-Ho. Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks. The Scientific World Journal No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1012803
American Medical Association (AMA)
Ko, Chien-Ho. Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1012803
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1012803